A Larval “Recruitment Kernel” to Predict Hatching Locations and Quantify Recruitment Patterns

Abstract

Larval recruitment, a critical component of population connectivity, has been under investigated compared to larval dispersal. We developed a backward‐in‐time Lagrangian particle tracking model to predict larval hatching locations and proposed a larval recruitment kernel, to quantify recruitment patterns. Combining field data and a hydrodynamic model, our backtracking model predicted Lake Whitefish (Coregonus clupeaformis) hatching locations in Lake Erie. We found a strong linear correlation (r = 0.95–0.98) between travel distance (i.e., distance along a trajectory) and pelagic larval duration (PLD), and a moderate correlation (r = 0.66–0.68) between linear distance (i.e., displacement) and PLD. This questions the wide use of PLD as a proxy for dispersal distance. We defined the recruitment kernel using the probability density function of the linear recruitment distance. Characteristics of the recruitment kernel, such as theoretical self‐recruitment, median‐recruitment distance, long‐distance recruitment, and openness convey significant information about population connectivity that are distinct from those derived using the well‐known dispersal kernel (e.g., theoretical local retention).